CISE Research Instrumentation: Active Learning for Text, Scene, and Biosequence Analysis

9617307 Cottrell，Garrison W. Charles Elkan，加州大学圣地亚哥分校 CISE研究仪器：文本，场景和生物序列分析的主动学习 通过自主学习适应不断变化的环境和不断变化的目标是UCSD人工智能实验室研究的中心主题。 通过这笔赠款获得的工作站，机器人和神经网络硬件将使最近开发的学习方法能够在四个大规模的现实世界应用中进行测试。信息检索的学习语义表示-嵌入式虚拟代理-MEME：一种新的序列分析软件工具-嵌入式代理中的实时人脸和物体识别。 第一个子项目将在国家TREC竞赛中应用新的算法来重新表示文档和响应人类对千兆字节文本的反馈。另一个项目将移植到全球网络软件代理，这些代理现在在模拟的人工生命环境中学习和交互。第三个项目将扩大新软件的规模，以发现DNA和蛋白质序列中的模式，成为一个能够自主分析整个基因组的工具。 最后，第四个项目将验证新的主动视觉方法识别和跟踪对象，通过实施这些方法在移动的机器人与可控相机。

9617307 Cottrell, Garrison W. Charles Elkan, University of California, San Diego CISE Research Instrumentation: Active Learning for Text, Scene, and Biosequence Analysis Adaptation to changing environments and changing goals through autonomous learning is a central theme of research in the artificial intelligence laboratory at UCSD. The workstations, robots, and neural network hardware acquired through this grant will enable recently developed learning methods to be tested in four large-scale, real-world applications.-Learning Semantic Representations for Information Retrieval-Embedded Virtual Agents-MEME: A New Software Tool for Sequence Analysis-Real Time Face and Object Recognition in Embedded Agents. The first subproject will apply new algorithms for document rerepresentation and response to human feedback to gigabytes of text in the national TREC competition. Another project will port to the worldwide web software agents that learn and interact now in a simulated artificial life environment. A third project will scale up new software for finding patterns in DNA and protein sequences into a tool capable of autonomously analyzing entire genomes. Finally, a fourth project will validate new active vision methods for recognizing and tracking objects by implementing these methods in mobile robots with controllable cameras.